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A Review of the ‘BMS’ Package for R with Focus on Jointness

by Shahram Amini 1,† and Christopher F. Parmeter 2,*,†
Daniels College of Business, University of Denver, Denver, CO 80208, USA
Department of Economics, University of Miami, Coral Gables, FL 33146, USA
Author to whom correspondence should be addressed.
We thank the editor and two anonymous referees for valuable feedback that improved the manuscript. The usual caveat applies. All code and data for this paper are available upon request.
Econometrics 2020, 8(1), 6;
Received: 2 September 2019 / Revised: 6 February 2020 / Accepted: 15 February 2020 / Published: 24 February 2020
(This article belongs to the Special Issue Bayesian and Frequentist Model Averaging)
We provide a general overview of Bayesian model averaging (BMA) along with the concept of jointness. We then describe the relative merits and attractiveness of the newest BMA software package, BMS, available in the statistical language R to implement a BMA exercise. BMS provides the user a wide range of customizable priors for conducting a BMA exercise, provides ample graphs to visualize results, and offers several alternative model search mechanisms. We also provide an application of the BMS package to equity premia and describe a simple function that can easily ascertain jointness measures of covariates and integrates with the BMS package. View Full-Text
Keywords: bayesian model averaging; Zellner’s g-prior; BMS bayesian model averaging; Zellner’s g-prior; BMS
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Amini, S.; Parmeter, C.F. A Review of the ‘BMS’ Package for R with Focus on Jointness. Econometrics 2020, 8, 6.

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